This thesis

Understanding the pathways of determinants of behaviour during pandemics in LMICs

Today’s presentation

• Factors associated with pandemic behaviour formation
• Theoretical Frameworks that (don’t) connect them
• Comparative structural modelling to inform a theory of pandemic behaviour
• Income vs health preservation
• Peer effects vs self selection

Financial constraints/ economic factors

• Classical economic theory suggests that individuals make rational choices under constraints, weighing the risk of infection against economic consequences (e.g., staying home vs. working).
• Health capital model (Grossman, 1972): Health is treated as an investment, with lower-income individuals discounting future health risks if avoiding them means immediate economic hardship.
• Prospect theory (Kahneman & Tversky, 1979): Individuals overweight immediate economic losses, making financially insecure people more likely to work despite health risks.

Health risk perceptions

• Health Belief Model (Rosenstock, 1974): Compliance is influenced by perceived severity, susceptibility, and benefits of protective actions.
• Risk perception (Slovic, 1987): Individuals facing immediate health threats (e.g., seeing loved ones infected) prioritize health over economic concerns.

Sociological factors

• Social capital theory: Stronger community networks lead to more collective action; U.S. counties with higher social capital had lower infection rates.
• Social influence theory: Individuals conform to peer behavior, sometimes overriding economic incentives, with political affiliations and community norms influencing compliance.

Knowledge and Trust

• The “knowledge-behavior gap” occurs when trust and biases interfere with knowledge-driven compliance.
• Trust in institutions: Compliance is higher when people trust expert sources, while peer/social media influence can sometimes outweigh expert advice.

Cultural factors

• Religion
• Etc

Institutional & political factors

Institutional trust (Fukuyama, 1995): High-trust societies show higher compliance, independent of financial constraints or personal risk perceptions.

Frameworks: Theory of plnned behaviour

REFERENCE/SOURCE

Frameworks: Health Belief Model

REFERENCE/SOURCE

What: Comparing and Extending TPB & HBM

Where: Global comparison

Comparing contexts across Africa, Latin America, Asia

How: Structural Equation Modelling I

Structural Equation Modelling

• Statistical technique that models complex relationships between observed (measured) and latent (unobserved) variables
  • Allows simultaneous estimation of direct and indirect effects in predicting preventive behavior
• Direct, indirect, and total effects analysed for all models
• Conducted at different waves for robustness checks and comparison

How: Structural Equation Modelling II

Confirmatory Factor Analysis (CFA)

• To validate latent constructs
• To operationalise constructs from survey items

Comparative Model Evaluation

• Predictive power assessment (variance explained in preventative behavior)
• Multi-group SEM for cross-country/SES/gender comparisons
• Akaike Information Criterion (AIC) & Bayesian Information Criterion (BIC) for model comparison

With: Meta/MIT C19 preventative health survey

Data

• Conducted via online surveys distributed to Facebook users globally
• Over 100k observations across 78 countries
• Weighted to account for probability of being FB user
• Contains data on all aforementioned factors
• Repeated cross sectional survey

Why: Contributions

Emprical contribution

• First large scale SEM across countries and waves
• Qunatification of uncertainity and relative importance of factors

Theoretical Contribution

• Informing more comprehensive framework of pandemic behaviour

Policy relevance

• Informs more efficient data collections during early phases of emergencies
• Informs ressource allocation efficiency

What: Testing the trade-off assumption

Widely assumed trade-off that individuals face

• Rationale behind cash transfer programmes
• Supported by economic theory
  • Grossman (19X)
  • Prospect Theory
• Empirical eveidence shows
  • Effects of income
  • Effects of risk perceptions
• Income and risk perception never modelled together in causal way

What II: Hypotheses

H1(2): Economic vulnerability (health risk perceptions) predict compliance.

H3: Individuals face a trade-off between preserving their health and their economic security.

Where: Malawi during waves 1 & 2 of C19

How: Challenges of traditional econometric approaches I

How: Challenges of traditional econometric approaches II

Strengths

• Interpretability
• Causal pathway identification

Limitations

• Exogeneity concerns
  • Either RCT or
  • Quasiexperimental approach difficult with income
  • Controlling requires many functional assumptions

How: Challenges of machine learning

Strengths

• Very flexible re functional forms
• Can handle large amounts of covariates

Limitations

• Overfitting concerns
• Causal pathway interpretation challenging

How: Double Debiased Machine Learning

Flexible ML to model covariate effect on treatment & outcome

• Predict outcome/ treatment seperately given vector of covariates using ML methods

Orthogonalised regression on residuals to estimate causal effect of treatment and outcome

• leftover variation of treatemnt -> leftover variation of outcome
  • Given no OVB
• Based on Neyman (19XX)

How: Double Debiased Machine Learning II

Cross-fitting to reduce overfitting and regularization bias

With: Malawi High Frequency Phone Survey

World Bank Malawi high frequency phone survey between 2020 and 2024

• Subset: 05/20-07/21
  • Only the first 13 rounds contain information about non-pharmaceutical mitigation behaviour
• Nationally representative sample of households based on the Malawi National Integrated Household Panel Survey from 2019
• 1729 participants at baseline & 8% attrition at R13
• Further weighting based on census data

Why: Contributions

Emprical contribution

• First causal investigation into health/income trade-off in context of protective measures during a pandemic
• Qunatification of trade-off and comparison over time

Theoretical Contribution

• Empirical test of widely-assumed trade-off assumption

Policy relevance

• Informs more efficient data collections during early phases of emergencies
• Informs ressource allocation efficiency